DocumentCode
3474027
Title
Notice of Retraction
A novel particle swarm optimization algorithm
Author
Xiaogang Wang ; Yan Bai ; Yue Li
Author_Institution
Wuhan Univ. of Sci. & Eng., Wuhan, China
Volume
1
fYear
2010
fDate
12-13 June 2010
Firstpage
408
Lastpage
411
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed in this paper. The standard Particle Swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c1 and c2, the inertia weight w, the maximum velocity Vmax, and the number of particles of the swarm. The purpose of this work is the development of an algorithm based on the Gaussian distribution, which improves the convergence ability of PSO without the necessity of tuning these parameters. The only parameter to be specified by the user is the number of particles. The Gaussian PSO algorithm was tested on a suite of well-known benchmark functions and the results were compared with the results of the standard PSO algorithm.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed in this paper. The standard Particle Swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c1 and c2, the inertia weight w, the maximum velocity Vmax, and the number of particles of the swarm. The purpose of this work is the development of an algorithm based on the Gaussian distribution, which improves the convergence ability of PSO without the necessity of tuning these parameters. The only parameter to be specified by the user is the number of particles. The Gaussian PSO algorithm was tested on a suite of well-known benchmark functions and the results were compared with the results of the standard PSO algorithm.
Keywords
Gaussian distribution; particle swarm optimisation; Gaussian PSO algorithm; Gaussian probability distribution; accelerating constants c1; accelerating constants c2; inertia weight w; maximum velocity Vmax; particle swarm optimization algorithm; Convergence; Optimization; Gaussian distribution; Particle Swarm Optimization; nonlinear optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5544119
Filename
5544119
Link To Document